Stochastic<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e462" altimg="si4.svg"><mml:msub><mml:mrow><mml:mi mathvariant="normal">H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math>finite-time control of networked cascade control systems under limited channels, network delays and packet dropouts

Elahi, Arezou; Alfi, Alireza

doi:10.1016/j.isatra.2019.07.020

Cited by 30 publications

(3 citation statements)

References 46 publications

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“…The 2D networked control system regards the 2D system as the controlled plane [6][7][8] and takes various network constraints as network characteristics [9][10][11]. Among these, the network packet dropout is a notable and unavoidable constraint in the 2D networked control system and has been a concern for many scholars.…”

Section: Introductionmentioning

confidence: 99%

Co-Design of Output-Based Event-Triggered Protocol and Sliding Mode Control for 2D Nonlinear Fornasini-Marchesini Network under Packet Dropouts

Jia

Zhang

2022

Electronics

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This paper focuses on the stability and sliding mode issues for the two-dimensional (2D) Fornasini-Marchesini (FMII) networked control system under packet dropouts. Firstly, the output-based 2D event-triggered strategy was constructed to alleviate information transmission pressure caused by limited network resources. Secondly, by considering the impact of packet dropouts, we propose an output-based 2D sliding mode controller and formulate the output-based 2D error-estimation scheme accordingly. Moreover, to get rid of the nonlinear coupling of the conditions (to guarantee the mean-square stability), we established an adaptive intelligence algorithm. Finally, we provide a numerical example to verify the effectiveness and practicability of the proposed algorithm and controller design.

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Section: Introductionmentioning

confidence: 99%

Co-Design of Output-Based Event-Triggered Protocol and Sliding Mode Control for 2D Nonlinear Fornasini-Marchesini Network under Packet Dropouts

Jia

Zhang

2022

Electronics

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“…Some random control theories are used to compensate for the time-delay. These compensation strategies include robust control, 14,15 neural network, [16][17][18] fuzzy control, 19,20 Markov jump system method, 21,22 state feedback control, 23,24 switching control method, 25 stochastic optimal control, 26,27 and other time-delay compensation methods. These compensation methods have achieved some progress.…”

Section: Introductionmentioning

confidence: 99%

Predictive control compensation for networked control system with time-delay

Tian

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study proposes a novel predictive control compensation method to deal with random time-delay in networked control system. Different from other compensation strategies, this study adopts different compensation strategies for input time-delay and output time-delay to improve the control effect. First, for the input channel time-delay, the corresponding buffer is set by the time-stamp in the packet. Combining the historical output value and the control variable, the fast implicit generalized predictive control algorithm is adopted to design the predictive controller to compensate for the input channel time-delay. Second, for the output channel time-delay, the controller cannot measure it. A control compensator is designed by adding a feedback loop. The output control variable of the predictive controller is adjusted by predicting the error between the actual control signal and the controller output at the historical sampling time, so as to compensate for the time-delay of the output channel. In addition, the stability of the proposed time-delay compensation method is analyzed. Finally, simulation and experimental results show that the proposed method has good control and compensation effect, and improves the output performance of the system. The networked control system with random time-delay is stable. Furthermore, the simulation results also show that this time-delay compensation method needs less computing time and is more suitable for the practical applications.

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“…In [25], Hu et al focus on the time-varying nonlinear systems existing the phenomena of multiple packet dropout, in which a combination of independent variables conforming to Bernoulli distribution is exploited to reflect the uncertain probabilities of multiple missing measurements. And, a novel algorithm is put forward for the networked cascade control system with the purpose of suppressing the adverse effects of delays, finite channel, and missing measurements in [30]. For the sake of simulating a more general network environment, taking the randomly occurring missing measurement into account is indispensable.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

et al. 2020

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This paper concentrates on the event-triggered H ∞ filter design for the discrete-time Markovian jump neural networks under random missing measurements and cyber attacks. Considering that the controlled system and the filtering can exchange information over a shared communication network which is vulnerable to the cyber attacks and has limited bandwidth, the event-triggered mechanism is proposed to relieve the communication burden of data transmission. A variable conforming to Bernoulli distribution is exploited to describe the stochastic phenomenon since the missing measurements occur with random probability. Furthermore, seeing that the communication networks are vulnerable to external malicious attacks, the transferred information via the shared communication network may be changed by the injected false information from the attackers. Based on the above consideration, sufficient conditions for the filtering error system to maintain asymptotically stable are provided with predefined H ∞ performance. In the end, three numerical examples are given to verify the proposed theoretical results.

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StochasticH∞finite-time control of networked cascade control systems under limited channels, network delays and packet dropouts

Cited by 30 publications

References 46 publications

Co-Design of Output-Based Event-Triggered Protocol and Sliding Mode Control for 2D Nonlinear Fornasini-Marchesini Network under Packet Dropouts

Co-Design of Output-Based Event-Triggered Protocol and Sliding Mode Control for 2D Nonlinear Fornasini-Marchesini Network under Packet Dropouts

Predictive control compensation for networked control system with time-delay

Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

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StochasticH∞finite-time control of networked cascade control systems under limited channels, network delays and packet dropouts

Cited by 30 publications

References 46 publications

Co-Design of Output-Based Event-Triggered Protocol and Sliding Mode Control for 2D Nonlinear Fornasini-Marchesini Network under Packet Dropouts

Co-Design of Output-Based Event-Triggered Protocol and Sliding Mode Control for 2D Nonlinear Fornasini-Marchesini Network under Packet Dropouts

Predictive control compensation for networked control system with time-delay

Event-Triggered H ∞ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks

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Product

Resources

About

Event-Triggered $H_{\infty}$ Filtering for Markovian Jump Neural Networks under Random Missing Measurements and Deception Attacks